package time.windows.overWindows;

import api.beans.SensorReading;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.table.api.Over;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.Tumble;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;

public class WindowTest1 {

    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);
        StreamTableEnvironment tableEnv = StreamTableEnvironment.create(env);
        String pathForm = "D:\\IdeaProjects\\springboot-flink-1\\flinkTutorial\\src\\main\\resources\\sensor.txt";
        String fileFormat = "csv";
        DataStream<String> inputStream = env.readTextFile(pathForm);
        DataStream<SensorReading> dataStream = inputStream.map(line -> {
            String[] fields = line.split(",");
            return new SensorReading(fields[0], new Long(fields[1]), new Double(fields[2]));
        }).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<SensorReading>(Time.seconds(2)) {
            @Override
            public long extractTimestamp(SensorReading element) {
                return element.getTimestamp() * 1000L;
            }
        });
        // 4. 将流转换成表，定义时间特性
        Table dataTable = tableEnv.fromDataStream(dataStream, "id, timestamp as ts, temperature as temp, rt.rowtime");

        dataTable.printSchema();

        tableEnv.createTemporaryView("sensor", dataTable);
        // 5. 窗口操作
        // 5.1 Over Window

        // table API
        Table overResult = dataTable.window(Over.partitionBy("id").orderBy("rt").preceding("2.rows").as("ow")).select("id, rt, id.count over ow, temp.avg over ow");
        //tableEnv.toAppendStream(overResult, Row.class).print("result");

        // SQL
        Table overSqlResult = tableEnv.sqlQuery("select id, rt, count(id) over ow, avg(temp) over ow from sensor window ow as (partition by id order by rt rows between 2 preceding and current row)");
        tableEnv.toRetractStream(overSqlResult, Row.class).print("sql");

        env.execute();
    }
}
